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Zhao Z, Feng Y, Wang M, Wei J, Tan T, Li R, Hu H, Wang M, Chen P, Gao X, Wei Y, Wang C, Gao Z, Jiang W, Zhou X, Li M, Wang C, Pang T, Yu Y. Investigating cortical complexity and connectivity in rats with schizophrenia. Front Neuroinform 2024; 18:1392271. [PMID: 39211912 PMCID: PMC11358091 DOI: 10.3389/fninf.2024.1392271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 07/29/2024] [Indexed: 09/04/2024] Open
Abstract
Background The above studies indicate that the SCZ animal model has abnormal gamma oscillations and abnormal functional coupling ability of brain regions at the cortical level. However, few researchers have focused on the correlation between brain complexity and connectivity at the cortical level. In order to provide a more accurate representation of brain activity, we studied the complexity of electrocorticogram (ECoG) signals and the information interaction between brain regions in schizophrenic rats, and explored the correlation between brain complexity and connectivity. Methods We collected ECoG signal from SCZ rats. The frequency domain and time domain functional connectivity of SCZ rats were evaluated by magnitude square coherence and mutual information (MI). Permutation entropy (PE) and permutation Lempel-Ziv complexity (PLZC) were used to analyze the complexity of ECoG, and the relationship between them was evaluated. In addition, in order to further understand the causal structure of directional information flow among brain regions, we used phase transfer entropy (PTE) to analyze the effective connectivity of the brain. Results Firstly, in the high gamma band, the complexity of brain regions in SCZ rats is higher than that in normal rats, and the neuronal activity is irregularity. Secondly, the information integration ability of SCZ rats decreased and the communication of brain network information was hindered at the cortical level. Finally, compared with normal rats, the causal relationship between brain regions of SCZ rats was closer, but the information interaction center was not clear. Conclusion The above findings suggest that at the cortical level, complexity and connectivity are valid biomarkers for identifying SCZ. This bridges the gap between peak potentials and EEG. This may help to understand the pathophysiological mechanisms at the cortical level in schizophrenics.
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Affiliation(s)
- Zongya Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Yifan Feng
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Menghan Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Jiarong Wei
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Tao Tan
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Ruijiao Li
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Heshun Hu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Mengke Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Peiqi Chen
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Xudong Gao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Yinping Wei
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Chang Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Zhixian Gao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Wenshuai Jiang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Xuezhi Zhou
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Mingcai Li
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Chong Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
| | - Ting Pang
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Center of Image and Signal Processing, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Yi Yu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
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Liu M, Yu C, Shi J, Xu Y, Li Z, Huang J, Si Z, Yao L, Yin K, Zhao Z. Effects of one-week bilateral cerebellar iTBS on resting-state functional brain network and multi-task attentional performance in healthy individuals: A randomized, sham-controlled trial. Neuroimage 2024; 295:120648. [PMID: 38761882 DOI: 10.1016/j.neuroimage.2024.120648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/29/2024] [Accepted: 05/13/2024] [Indexed: 05/20/2024] Open
Abstract
BACKGROUND Cerebellar intermittent theta burst stimulation (iTBS) modulates the excitability of the cerebral cortex and may enhance attentional performance. To date, few studies have conducted iTBS on healthy subjects for one week and used electroencephalography (EEG) to investigate the effect of multiple stimulation sessions on resting-state functional brain networks and the daily stimulation effect on attentional performance. METHODS 16 healthy subjects participated in a one-week experiment, receiving bilateral cerebellar iTBS or sham stimulation and engaging in multi-task attentional training. The primary measures were the one-week attentional performance and pre- and post-experiment resting-state EEG activities. Amplitude Envelope Correlation (AEC) was used to construct the functional connectivity in the eye-open (EO) and eye-closed (EC) phases. RESULTS At least three sessions of iTBS were required to enhance multi-task performance significantly, whereas only one or two sessions failed to elicit the improvement. Compared with the control group, iTBS induced significant changes in PSD, AEC functional connectivity, and AEC network properties during the EO phase, while it had little effect during the EC phase. During the EO phase, the network property changes of the iTBS subject were correlated with improved attentional performance. CONCLUSION The multi-task performance requires multiple stimulations to enhance. iTBS affects the resting-state alpha band brain activities during the EO rather than the EC phase. The AEC network properties may serve as a biomarker to assess the attentional potential of healthy subjects.
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Affiliation(s)
- Meiliang Liu
- School of Artificial Intelligence, Beijing Normal University, Beijing, China.
| | - Chao Yu
- Nanjing Research Institute of Electronics Technology, Nanjing, China.
| | - Jinping Shi
- Department of Neurology, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Yunfang Xu
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Zijin Li
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Junhao Huang
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Zhengye Si
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Li Yao
- School of Artificial Intelligence, Beijing Normal University, Beijing, China
| | - Kuiying Yin
- Nanjing Research Institute of Electronics Technology, Nanjing, China.
| | - Zhiwen Zhao
- School of Artificial Intelligence, Beijing Normal University, Beijing, China; Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, China.
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3
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Chang Y, Wang X, Liao J, Chen S, Liu X, Liu S, Ming D. Temporal hyper-connectivity and frontal hypo-connectivity within gamma band in schizophrenia: A resting state EEG study. Schizophr Res 2024; 264:220-230. [PMID: 38183959 DOI: 10.1016/j.schres.2023.12.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 11/12/2023] [Accepted: 12/16/2023] [Indexed: 01/08/2024]
Abstract
OBJECTIVE The brain network serves as the physiological foundation for information processing of the brain. Many studies have reported abnormalities of gamma oscillations in Schizophrenia. The aim of this study was to investigate the gamma-band connectivity in Schizophrenia patients. METHODS We recorded the resting state electroencephalogram (EEG) for 15 schizophrenia patients with refractory auditory hallucinations and 14 healthy controls, with eyes open and closed. The brain network was constructed based on weighted phase lag index for gamma band. Whole scalp metrics (clustering coefficient, global efficiency and local efficiency) and local region metrics (degree and betweenness centrality) in the frontal and temporal lobes were computed. Correlation analyses between network metrics and symptom scales were examined to find associations with symptom severity. RESULTS Schizophrenia patients had larger global efficiency and local efficiency (p < 0.05) with eyes closed, probably representing greater brain activity and information exchange. For degree and betweenness centrality, schizophrenia patients showed an increase (p < 0.05) in the temporal lobe but a decrease (p < 0.05) in the frontal lobe with eyes closed and open, potentially account for the patients' symptoms such as hallucinations and thought disorders. Local efficiency and frontal lobe degree were positively and negatively correlated with the scales, respectively (both p < 0.05). CONCLUSIONS Altered connectivity of the resting state brain network has been revealed and may be associated with the core symptoms of schizophrenia. Our study provides promising evidence for the investigation of the pathological basis of Schizophrenia and could aid in objective diagnosis.
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Affiliation(s)
- Yuan Chang
- Tianjin University, Academy of Medical Engineering and Translational Medicine, Tianjin, China
| | - Xiaojuan Wang
- Tianjin University, Academy of Medical Engineering and Translational Medicine, Tianjin, China
| | - Jingmeng Liao
- Tianjin University, Academy of Medical Engineering and Translational Medicine, Tianjin, China
| | - Sitong Chen
- Tianjin University, Academy of Medical Engineering and Translational Medicine, Tianjin, China
| | - Xiaoya Liu
- Tianjin University, Academy of Medical Engineering and Translational Medicine, Tianjin, China
| | - Shuang Liu
- Tianjin University, Academy of Medical Engineering and Translational Medicine, Tianjin, China.
| | - Dong Ming
- Tianjin University, Academy of Medical Engineering and Translational Medicine, Tianjin, China
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Aydın S, Onbaşı L. Graph theoretical brain connectivity measures to investigate neural correlates of music rhythms associated with fear and anger. Cogn Neurodyn 2024; 18:49-66. [PMID: 38406195 PMCID: PMC10881947 DOI: 10.1007/s11571-023-09931-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 10/19/2022] [Accepted: 01/09/2023] [Indexed: 01/26/2023] Open
Abstract
The present study tests the hypothesis that emotions of fear and anger are associated with distinct psychophysiological and neural circuitry according to discrete emotion model due to contrasting neurotransmitter activities, despite being included in the same affective group in many studies due to similar arousal-valance scores of them in emotion models. EEG data is downloaded from OpenNeuro platform with access number of ds002721. Brain connectivity estimations are obtained by using both functional and effective connectivity estimators in analysis of short (2 sec) and long (6 sec) EEG segments across the cortex. In tests, discrete emotions and resting-states are identified by frequency band specific brain network measures and then contrasting emotional states are deep classified with 5-fold cross-validated Long Short Term Memory Networks. Logistic regression modeling has also been examined to provide robust performance criteria. Commonly, the best results are obtained by using Partial Directed Coherence in Gamma (31.5 - 60.5 H z ) sub-bands of short EEG segments. In particular, Fear and Anger have been classified with accuracy of 91.79%. Thus, our hypothesis is supported by overall results. In conclusion, Anger is found to be characterized by increased transitivity and decreased local efficiency in addition to lower modularity in Gamma-band in comparison to fear. Local efficiency refers functional brain segregation originated from the ability of the brain to exchange information locally. Transitivity refer the overall probability for the brain having adjacent neural populations interconnected, thus revealing the existence of tightly connected cortical regions. Modularity quantifies how well the brain can be partitioned into functional cortical regions. In conclusion, PDC is proposed to graph theoretical analysis of short EEG epochs in presenting robust emotional indicators sensitive to perception of affective sounds.
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Affiliation(s)
- Serap Aydın
- Department of Biophysics, Faculty of Medicine, Hacettepe University, Sıhhiye, Ankara, Turkey
| | - Lara Onbaşı
- School of Medicine, Hacettepe University, Sıhhiye, Ankara, Turkey
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5
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Zhang H, Zhou QQ, Chen H, Hu XQ, Li WG, Bai Y, Han JX, Wang Y, Liang ZH, Chen D, Cong FY, Yan JQ, Li XL. The applied principles of EEG analysis methods in neuroscience and clinical neurology. Mil Med Res 2023; 10:67. [PMID: 38115158 PMCID: PMC10729551 DOI: 10.1186/s40779-023-00502-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 11/23/2023] [Indexed: 12/21/2023] Open
Abstract
Electroencephalography (EEG) is a non-invasive measurement method for brain activity. Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural signals, EEG has aroused much interest in scientific research and medical fields. This article reviews the types of EEG signals, multiple EEG signal analysis methods, and the application of relevant methods in the neuroscience field and for diagnosing neurological diseases. First, three types of EEG signals, including time-invariant EEG, accurate event-related EEG, and random event-related EEG, are introduced. Second, five main directions for the methods of EEG analysis, including power spectrum analysis, time-frequency analysis, connectivity analysis, source localization methods, and machine learning methods, are described in the main section, along with different sub-methods and effect evaluations for solving the same problem. Finally, the application scenarios of different EEG analysis methods are emphasized, and the advantages and disadvantages of similar methods are distinguished. This article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives, provide references for subsequent research, and summarize current issues and prospects for the future.
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Affiliation(s)
- Hao Zhang
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Qing-Qi Zhou
- College of Electrical and Control Engineering, North China University of Technology, Beijing, 100041, China
| | - He Chen
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China
| | - Xiao-Qing Hu
- Department of Psychology, the State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, 999077, China
- HKU-Shenzhen Institute of Research and Innovation, Shenzhen, 518057, Guangdong, China
| | - Wei-Guang Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Yang Bai
- Department of Rehabilitation Medicine, the First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
- Rehabilitation Medicine Clinical Research Center of Jiangxi Province, Nanchang, 330006, China
| | - Jun-Xia Han
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, 100048, China
| | - Yao Wang
- School of Communication Science, Beijing Language and Culture University, Beijing, 100083, China
| | - Zhen-Hu Liang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China.
| | - Dan Chen
- School of Computer Science, Wuhan University, Wuhan, 430072, China.
| | - Feng-Yu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116081, Liaoning, China.
| | - Jia-Qing Yan
- College of Electrical and Control Engineering, North China University of Technology, Beijing, 100041, China.
| | - Xiao-Li Li
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China.
- Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou, 510335, China.
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6
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Yin G, Chang Y, Zhao Y, Liu C, Yin M, Fu Y, Shi D, Wang L, Jin L, Huang J, Li D, Niu Y, Wang B, Tan S. Automatic recognition of schizophrenia from brain-network features using graph convolutional neural network. Asian J Psychiatr 2023; 87:103687. [PMID: 37418809 DOI: 10.1016/j.ajp.2023.103687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 06/25/2023] [Accepted: 06/30/2023] [Indexed: 07/09/2023]
Abstract
Schizophrenia is a severe mental illness that imposes considerable economic burden on families and society. However, its clinical diagnosis primarily relies on scales and doctors' clinical experience and lacks an objective and accurate diagnostic approach. In recent years, graph convolutional neural networks (GCN) have been used to assist in psychiatric diagnosis owing to their ability to learn spatial-association information. Therefore, this study proposes a schizophrenia automatic recognition model based on graph convolutional neural network. Herein, the resting-state electroencephalography (EEG) data of 103 first-episode schizophrenia patients and 92 normal controls (NCs) were obtained. The automatic recognition model was trained with a nodal feature matrix that comprised the time and frequency-domain features of the EEG signals and local features of the brain network. The most significant regions that contributed to the model classification were identified, and the correlation between the node topological features of each significant region and clinical evaluation metrics was explored. Experiments were conducted to evaluate the performance of the model using 10-fold cross-validation. The best performance in the theta frequency band with a 6 s epoch length and phase-locked value. The recognition accuracy was 90.01%. The most significant region for identifying with first-episode schizophrenia patients and NCs was located in the parietal lobe. The results of this study verify the applicability of the proposed novel method for the identification and diagnosis of schizophrenia.
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Affiliation(s)
- Guimei Yin
- College of Computer Science and Technology, Taiyuan Normal University, City Jinzhong 030619 Shanxi, China
| | - Ying Chang
- Departs of Ultrasonography, Xuan Wu Hospital, Capital Medical University, Beijing 100053, China
| | - Yanli Zhao
- Peking University Huilonguan Clinical Medical School, Psychiatry Research Center, Beijing Huilongguan Hospital, Beijing 100096, China
| | - Chenxu Liu
- College of Computer Science and Technology, Taiyuan Normal University, City Jinzhong 030619 Shanxi, China
| | - Mengzhen Yin
- College of Computer Science and Technology, Taiyuan Normal University, City Jinzhong 030619 Shanxi, China
| | - Yongcan Fu
- College of Computer Science and Technology, Taiyuan Normal University, City Jinzhong 030619 Shanxi, China
| | - Dongli Shi
- College of Computer Science and Technology, Taiyuan Normal University, City Jinzhong 030619 Shanxi, China
| | - Lin Wang
- College of Computer Science and Technology, Taiyuan Normal University, City Jinzhong 030619 Shanxi, China
| | - Lizhong Jin
- Taiyuan University of Science and Technology, Taiyuan 030024 Shanxi, China
| | - Jie Huang
- Peking University Huilonguan Clinical Medical School, Psychiatry Research Center, Beijing Huilongguan Hospital, Beijing 100096, China
| | - Dandan Li
- Taiyuan University of Technology, Jinzhong 030600 Shanxi, China
| | - Yan Niu
- Taiyuan University of Technology, Jinzhong 030600 Shanxi, China
| | - Bin Wang
- Taiyuan University of Technology, Jinzhong 030600 Shanxi, China.
| | - Shuping Tan
- Peking University Huilonguan Clinical Medical School, Psychiatry Research Center, Beijing Huilongguan Hospital, Beijing 100096, China.
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Kose MR, Ahirwal MK, Atulkar M. Weighted ordinal connection based functional network classification for schizophrenia disease detection using EEG signal. Phys Eng Sci Med 2023; 46:1055-1070. [PMID: 37222953 DOI: 10.1007/s13246-023-01273-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 05/02/2023] [Indexed: 05/25/2023]
Abstract
A brain connectivity network (BCN) is an advanced approach to examining brain functionality in various conditions. However, the predictability of the BCN is affected by the connectivity measure used for the network construction. Various connectivity measures available in the literature differ according to the domain of their working data. The application of random connectivity measures might result in an inefficient BCN that ultimately hampers its predictability. Therefore, selecting an appropriate functional connectivity metric is crucial in clinical as well as cognitive neuroscience. In parallel to this, an effective network identifier plays a vital role in distinguishing different brain states. Hence, the objective of this paper is two-fold, which includes identifying suitable connectivity measures and proposing an efficient network identifier. For this, the weighted BCN (WBCN) is constructed using multiple connectivity measures like correlation coefficient (r), coherence (COH), phase-locking value (PLV), and mutual information (MI) from electroencephalogram (EEG) signals. The most recent technique for feature extraction, i.e., weighted ordinal connections, has been applied to EEG-based BCN. EEG signals data has been taken from the schizophrenia disease database. Further, several classification algorithms such as k-nearest neighbours (KNN), support vector machine (SVM) with linear, radial basis function and polynomial kernels, random forest (RF), and 1D convolutional neural network (CNN1D) are used to classify the brain states based on extracted features. In classification, 90% accuracy is achieved by the CNN1D classifier with WBCN based on the coherence connectivity measure. The study also provides a structural analysis of the BCN.
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Affiliation(s)
- Mangesh R Kose
- Department of Computer Application, NIT, Raipur, 492010, CG, India.
| | - Mitul K Ahirwal
- Department of Computer Science and Engineering, MANIT, Bhopal, 462003, MP, India
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Nasretdinov A, Barrientos SA, Brys I, Halje P, Petersson P. Systems-level analysis of local field potentials reveals differential effects of lysergic acid diethylamide and ketamine on neuronal activity and functional connectivity. Front Neurosci 2023; 17:1175575. [PMID: 37287794 PMCID: PMC10242129 DOI: 10.3389/fnins.2023.1175575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 05/05/2023] [Indexed: 06/09/2023] Open
Abstract
Psychedelic substances have in recent years attracted considerable interest as potential treatments for several psychiatric conditions, including depression, anxiety, and addiction. Imaging studies in humans point to a number of possible mechanisms underlying the acute effects of psychedelics, including changes in neuronal firing rates and excitability as well as alterations in functional connectivity between various brain nodes. In addition, animal studies using invasive recordings, have suggested synchronous high-frequency oscillations involving several brain regions as another key feature of the psychedelic brain state. To better understand how the imaging data might be related to high-resolution electrophysiological measurements, we have here analyzed the aperiodic part of the local field potential (LFP) in rodents treated with a classic psychedelic (LSD) or a dissociative anesthetic (ketamine). In addition, functional connectivity, as quantified by mutual information measures in the LFP time series, has been assessed with in and between different structures. Our data suggest that the altered brain states of LSD and ketamine are caused by different underlying mechanisms, where LFP power shifts indicate increased neuronal activity but reduced connectivity following ketamine, while LSD also leads to reduced connectivity but without an accompanying change in LFP broadband power.
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Affiliation(s)
- Azat Nasretdinov
- The Group for Integrative Neurophysiology, Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | - Sebastian A. Barrientos
- The Group for Integrative Neurophysiology, Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
- The Group for Integrative Neurophysiology and Neurotechnology, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Ivani Brys
- The Group for Integrative Neurophysiology, Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
- The Group for Integrative Neurophysiology and Neurotechnology, Department of Experimental Medical Science, Lund University, Lund, Sweden
- Postgraduate Program in Psychology, Health, and Biological Sciences, Federal University of Vale do São Francisco (UNIVASF), Petrolina, Brazil
| | - Pär Halje
- The Group for Integrative Neurophysiology and Neurotechnology, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Per Petersson
- The Group for Integrative Neurophysiology, Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
- The Group for Integrative Neurophysiology and Neurotechnology, Department of Experimental Medical Science, Lund University, Lund, Sweden
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Tang Y, Chen D, Wu J, Tu W, Monaghan JJM, Sowman P, Mcalpine D. Functional connectivity learning via Siamese-based SPD matrix representation of brain imaging data. Neural Netw 2023; 163:272-285. [PMID: 37086544 DOI: 10.1016/j.neunet.2023.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 04/24/2023]
Abstract
Measurement of brain functional connectivity has become a dominant approach to explore the interaction dynamics between brain regions of subjects under examination. Conventional functional connectivity measures largely originate from deterministic models on empirical analysis, usually demanding application-specific settings (e.g., Pearson's Correlation and Mutual Information). To bridge the technical gap, this study proposes a Siamese-based Symmetric Positive Definite (SPD) Matrix Representation framework (SiameseSPD-MR) to derive the functional connectivity of brain imaging data (BID) such as Electroencephalography (EEG), thus the alternative application-independent measure (in the form of SPD matrix) can be automatically learnt: (1) SiameseSPD-MR first exploits graph convolution to extract the representative features of BID with the adjacency matrix computed considering the anatomical structure; (2) Adaptive Gaussian kernel function then applies to obtain the functional connectivity representations from the deep features followed by SPD matrix transformation to address the intrinsic functional characteristics; and (3) Two-branch (Siamese) networks are combined via an element-wise product followed by a dense layer to derive the similarity between the pairwise inputs. Experimental results on two EEG datasets (autism spectrum disorder, emotion) indicate that (1) SiameseSPD-MR can capture more significant differences in functional connectivity between neural states than the state-of-the-art counterparts do, and these findings properly highlight the typical EEG characteristics of ASD subjects, and (2) the obtained functional connectivity representations conforming to the proposed measure can act as meaningful markers for brain network analysis and ASD discrimination.
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Affiliation(s)
- Yunbo Tang
- School of Computer Science, Wuhan University, Wuhan, China
| | - Dan Chen
- School of Computer Science, Wuhan University, Wuhan, China.
| | - Jia Wu
- School of Computing, Macquarie University, Sydney, Australia
| | - Weiping Tu
- School of Computer Science, Wuhan University, Wuhan, China
| | | | - Paul Sowman
- School of Computing, Macquarie University, Sydney, Australia
| | - David Mcalpine
- School of Computing, Macquarie University, Sydney, Australia
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10
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Zhao Z, Li Q, Shao Y, Tan C, Zhou C, Fan H, Li L, Zheng C, Gao X. Prediction of inlet SO 2 concentration of wet flue gas desulfurization (WFGD) by operation parameters of coal-fired boiler. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:53089-53102. [PMID: 36853530 DOI: 10.1007/s11356-023-25988-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
Circulating fluidized bed (CFB) boilers with wet flue gas desulfurization (WFGD) system is a popular technology for SO2 removal in the coal-fired thermal power plant. However, the long response time of continues emission monitoring system (CEMS) and the hardness of continuously monitoring the coal properties leads to the difficulties for controlling WFGD. It is important to build a model that is adaptable to the fluctuation of load and coal properties, which can obtain the SO2 concentration ahead CEMS, without relying on coal properties. In this paper, a prediction model of inlet SO2 concentration of WFGD considering the delay between the features and target based on long-short term memory (LSTM) network with auto regression feature is established. The SO2 concentration can be obtained 90 s earlier than CEMS. The model shows good adaptability to the fluctuation of SO2 concentration and coal properties. The root-mean-squared error (RMSE) and R squared (R2) of the model are 30.11 mg/m3 and 0.986, respectively. Meanwhile, a real-time prediction system is built on the 220 t/h unit. A field test for long-term operation has been conducted. The prediction system is able to continuously and accurately predict the inlet SO2 concentration of the WFGD, which can provide the operators with an accurate reference for the control of WFGD.
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Affiliation(s)
- Zhongyang Zhao
- State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Zhejiang University, Hangzhou, 310027, China
| | - Qinwu Li
- State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Zhejiang University, Hangzhou, 310027, China
| | - Yuhao Shao
- State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Zhejiang University, Hangzhou, 310027, China
| | - Chang Tan
- State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Zhejiang University, Hangzhou, 310027, China
| | - Can Zhou
- State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Zhejiang University, Hangzhou, 310027, China
- Jiaxing Research Institute, Zhejiang University, Jaixing, 314000, China
| | - Haidong Fan
- State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Zhejiang University, Hangzhou, 310027, China
| | - Lianming Li
- Jiaxing Xinjia'aisi Thermal Power Co., Ltd, Jiaxing, 314000, China
| | - Chenghang Zheng
- State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Zhejiang University, Hangzhou, 310027, China.
- Jiaxing Research Institute, Zhejiang University, Jaixing, 314000, China.
| | - Xiang Gao
- State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Zhejiang University, Hangzhou, 310027, China
- Jiaxing Research Institute, Zhejiang University, Jaixing, 314000, China
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11
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Khare SK, Bajaj V, Acharya UR. SchizoNET: a robust and accurate Margenau-Hill time-frequency distribution based deep neural network model for schizophrenia detection using EEG signals. Physiol Meas 2023; 44. [PMID: 36787641 DOI: 10.1088/1361-6579/acbc06] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 02/14/2023] [Indexed: 02/16/2023]
Abstract
Objective.Schizophrenia (SZ) is a severe chronic illness characterized by delusions, cognitive dysfunctions, and hallucinations that impact feelings, behaviour, and thinking. Timely detection and treatment of SZ are necessary to avoid long-term consequences. Electroencephalogram (EEG) signals are one form of a biomarker that can reveal hidden changes in the brain during SZ. However, the EEG signals are non-stationary in nature with low amplitude. Therefore, extracting the hidden information from the EEG signals is challenging.Approach.The time-frequency domain is crucial for the automatic detection of SZ. Therefore, this paper presents the SchizoNET model combining the Margenau-Hill time-frequency distribution (MH-TFD) and convolutional neural network (CNN). The instantaneous information of EEG signals is captured in the time-frequency domain using MH-TFD. The time-frequency amplitude is converted to two-dimensional plots and fed to the developed CNN model.Results.The SchizoNET model is developed using three different validation techniques, including holdout, five-fold cross-validation, and ten-fold cross-validation techniques using three separate public SZ datasets (Dataset 1, 2, and 3). The proposed model achieved an accuracy of 97.4%, 99.74%, and 96.35% on Dataset 1 (adolescents: 45 SZ and 39 HC subjects), Dataset 2 (adults: 14 SZ and 14 HC subjects), and Dataset 3 (adults: 49 SZ and 32 HC subjects), respectively. We have also evaluated six performance parameters and the area under the curve to evaluate the performance of our developed model.Significance.The SchizoNET is robust, effective, and accurate, as it performed better than the state-of-the-art techniques. To the best of our knowledge, this is the first work to explore three publicly available EEG datasets for the automated detection of SZ. Our SchizoNET model can help neurologists detect the SZ in various scenarios.
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Affiliation(s)
- Smith K Khare
- Electrical and Computer Engineering Department, Aarhus University, Denmark
| | - Varun Bajaj
- Discipline of Electronics and Communication Engineering, Indian Institute of Information Technology, Design, and Manufacturing (IIITDM) Jabalpur, India
| | - U Rajendra Acharya
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, Australia.,Department of Biomedical Engineering, School of Science and Technology, University of Social Sciences, Singapore.,Department of Biomedical Informatics and Medical Engineering, Asia University, Taiwan.,Distinguished Professor, Kumamoto University, Japan.,Adjunct Professor, University of Malaya, Malaysia
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12
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Klepl D, He F, Wu M, Blackburn DJ, Sarrigiannis P. EEG-Based Graph Neural Network Classification of Alzheimer's Disease: An Empirical Evaluation of Functional Connectivity Methods. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2651-2660. [PMID: 36067099 DOI: 10.1109/tnsre.2022.3204913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Alzheimer's disease (AD) is the leading form of dementia worldwide. AD disrupts neuronal pathways and thus is commonly viewed as a network disorder. Many studies demonstrate the power of functional connectivity (FC) graph-based biomarkers for automated diagnosis of AD using electroencephalography (EEG). However, various FC measures are commonly utilised, as each aims to quantify a unique aspect of brain coupling. Graph neural networks (GNN) provide a powerful framework for learning on graphs. While a growing number of studies use GNN to classify EEG brain graphs, it is unclear which method should be utilised to estimate the brain graph. We use eight FC measures to estimate FC brain graphs from sensor-level EEG signals. GNN models are trained in order to compare the performance of the selected FC measures. Additionally, three baseline models based on literature are trained for comparison. We show that GNN models perform significantly better than the other baseline models. Moreover, using FC measures to estimate brain graphs improves the performance of GNN compared to models trained using a fixed graph based on the spatial distance between the EEG sensors. However, no FC measure performs consistently better than the other measures. The best GNN reaches 0.984 area under sensitivity-specificity curve (AUC) and 92% accuracy, whereas the best baseline model, a convolutional neural network, has 0.924 AUC and 84.7% accuracy.
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13
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O'Keeffe R, Shirazi SY, Bilaloglu S, Jahed S, Bighamian R, Raghavan P, Atashzar SF. Nonlinear functional muscle network based on information theory tracks sensorimotor integration post stroke. Sci Rep 2022; 12:13029. [PMID: 35906239 PMCID: PMC9338017 DOI: 10.1038/s41598-022-16483-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 07/11/2022] [Indexed: 11/28/2022] Open
Abstract
Sensory information is critical for motor coordination. However, understanding sensorimotor integration is complicated, especially in individuals with impairment due to injury to the central nervous system. This research presents a novel functional biomarker, based on a nonlinear network graph of muscle connectivity, called InfoMuNet, to quantify the role of sensory information on motor performance. Thirty-two individuals with post-stroke hemiparesis performed a grasp-and-lift task, while their muscle activity from 8 muscles in each arm was measured using surface electromyography. Subjects performed the task with their affected hand before and after sensory exposure to the task performed with the less-affected hand. For the first time, this work shows that InfoMuNet robustly quantifies changes in functional muscle connectivity in the affected hand after exposure to sensory information from the less-affected side. > 90% of the subjects conformed with the improvement resulting from this sensory exposure. InfoMuNet also shows high sensitivity to tactile, kinesthetic, and visual input alterations at the subject level, highlighting its potential use in precision rehabilitation interventions.
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Affiliation(s)
- Rory O'Keeffe
- Department of Electrical and Computer Engineering, New York University, New York, NY, USA
| | - Seyed Yahya Shirazi
- Department of Electrical and Computer Engineering, New York University, New York, NY, USA
| | - Seda Bilaloglu
- Department of Medicine, New York University Langone Health, New York, NY, USA
| | - Shayan Jahed
- Department of Electrical and Computer Engineering, New York University, New York, NY, USA
| | - Ramin Bighamian
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, Silver Spring, MD, USA
| | - Preeti Raghavan
- Departments of Physical Medicine and Rehabilitation and Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - S Farokh Atashzar
- Department of Electrical and Computer Engineering, New York University, New York, NY, USA.
- Department of Mechanical and Aerospace Engineering, New York University, New York, NY, USA.
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14
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Recognition of the Multi-class Schizophrenia Based on the Resting-State EEG Network Topology. Brain Topogr 2022; 35:495-506. [PMID: 35849250 DOI: 10.1007/s10548-022-00907-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 06/02/2022] [Indexed: 11/02/2022]
Abstract
The clinical therapy of schizophrenia (SCZ) replies on the corresponding accurate and reliable recognition. Although efforts have been paid, the diagnosis of SCZ is still roughly subjective, it is thus urgent to search for related objective physiological parameters. Motivated by the great potential of resting-state networks in underling the brain deficits among different SCZ groups, in this study, we then developed a multi-class feature extraction approach that could effectively extract the spatial network topology and facilitate the recognition of the SCZ, by combining a network structure based supervised learning with an ensemble co-decision strategy. The results demonstrated that the multi-class spatial pattern of the network (MSPN) features outperformed the other conventional electrophysiological features, such as relative power spectrums and network properties, and achieved the highest classification accuracy of 71.58% in the alpha band. These findings did validate that the resting-state MSPN is a promising tool for the clinical assessment of the SCZ.
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15
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qEEG Analysis in the Diagnosis of Alzheimer’s Disease: A Comparison of Functional Connectivity and Spectral Analysis. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12105162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Alzheimer’s disease (AD) is a brain disorder that is mainly characterized by a progressive degeneration of neurons in the brain and decline of cognitive abilities. This study compared an FFT-based spectral analysis against a functional connectivity analysis for the diagnosis of AD. Both quantitative methods were applied on an EEG dataset including 20 diagnosed AD patients and 20 age-matched healthy controls (HC). The obtained results showed an advantage of the functional connectivity analysis when compared to the spectral analysis; while the latter could not find any significant differences between the AD and HC groups, the functional connectivity analysis showed statistically higher synchronization levels in the AD group in the lower frequency bands (delta and theta), suggesting a ‘phase-locked’ state in AD-affected brains. Further comparison of functional connectivity between the homotopic regions confirmed that the traits of AD were localized to the centro-parietal and centro-temporal areas in the theta frequency band (4–8 Hz). This study applies a neural metric for Alzheimer’s detection from a data science perspective rather than from a neuroscience one and shows that the combination of bipolar derivations with phase synchronization yields similar results to comparable studies employing alternative analysis methods.
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16
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Yin G, Li Q, Zhao Z, Li L, Yao L, Weng W, Zheng C, Lu J, Gao X. Dynamic NO x emission prediction based on composite models adapt to different operating conditions of coal-fired utility boilers. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:13541-13554. [PMID: 34595703 DOI: 10.1007/s11356-021-16543-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 09/10/2021] [Indexed: 06/13/2023]
Abstract
An accurate NOx concentration prediction model plays an important role in low NOx emission control in power stations. Predicting NOx in advance is of great significance in satisfying stringent environmental policies. This study aims to accurately predict the NOx emission concentration at the outlet of boilers on different operating conditions to support the DeNOx procedure. Through mutual information analysis, suitable features are selected to build models. Long short-term memory (LSTM) models are utilized to predict NOx concentration at the boiler's outlet from selected input features and exhibit power in fitting multivariable coupling, nonlinear, and large time-delay systems. Moreover, a composite LSTM model composed of models on different operating conditions, like steady-state and transient-state condition, is prosed. Results of one whole day of typical operating data show that the accuracy of the NOx concentration and fluctuation trend prediction based on this composite model is superior to that using a single LSTM model and other non-time-sequence models. The root mean square error (RMSE) and R2 of the composite LSTM model are 3.53 mg/m3 and 0.89, respectively, which are better than those of a single LSTM (i.e., 5.50 mg/m3 and 0.78, respectively).
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Affiliation(s)
- Guihao Yin
- State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Institute for Thermal Power Engineering, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, China
| | - Qinwu Li
- Zhejiang HOPE Environmental Protection Engineering Co. Ltd., Hangzhou, 310013, China
| | - Zhongyang Zhao
- State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Institute for Thermal Power Engineering, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, China
| | - Lianmin Li
- State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Institute for Thermal Power Engineering, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, China
- Jiaxing Xinjia'aisi Thermal Power Co., Ltd., Jiaxing, 314000, China
| | - Longchao Yao
- State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Institute for Thermal Power Engineering, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, China
| | - Weiguo Weng
- State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Institute for Thermal Power Engineering, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, China
| | - Chenghang Zheng
- State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Institute for Thermal Power Engineering, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, China.
| | - Jiangang Lu
- College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Xiang Gao
- State Key Laboratory of Clean Energy Utilization, State Environmental Protection Center for Coal-Fired Air Pollution Control, Institute for Thermal Power Engineering, Zhejiang University, 38 Zheda Road, Hangzhou, 310027, China
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17
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Cao J, Zhao Y, Shan X, Wei H, Guo Y, Chen L, Erkoyuncu JA, Sarrigiannis PG. Brain functional and effective connectivity based on electroencephalography recordings: A review. Hum Brain Mapp 2022; 43:860-879. [PMID: 34668603 PMCID: PMC8720201 DOI: 10.1002/hbm.25683] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 09/10/2021] [Accepted: 09/27/2021] [Indexed: 12/02/2022] Open
Abstract
Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG-based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools. This article reviews EEG-based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time-based, and frequency-based or time-frequency-based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.
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Affiliation(s)
- Jun Cao
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfield
| | - Yifan Zhao
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfield
| | - Xiaocai Shan
- School of Aerospace, Transport and ManufacturingCranfield UniversityCranfield
- Institute of Geology and Geophysics, Chinese Academy of SciencesBeijingChina
| | - Hua‐liang Wei
- Department of Automatic Control and Systems EngineeringUniversity of SheffieldSheffieldUK
| | - Yuzhu Guo
- School of Automation Science and Electrical EngineeringBeihang UniversityBeijingChina
| | - Liangyu Chen
- Department of NeurosurgeryShengjing Hospital of China Medical UniversityShenyangChina
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18
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A survey of brain network analysis by electroencephalographic signals. Cogn Neurodyn 2022; 16:17-41. [PMID: 35126769 PMCID: PMC8807775 DOI: 10.1007/s11571-021-09689-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 04/25/2021] [Accepted: 05/31/2021] [Indexed: 02/03/2023] Open
Abstract
Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.
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19
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Wu X, Zheng WL, Li Z, Lu BL. Investigating EEG-based functional connectivity patterns for multimodal emotion recognition. J Neural Eng 2022; 19. [DOI: 10.1088/1741-2552/ac49a7] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 01/10/2022] [Indexed: 02/04/2023]
Abstract
Abstract
Objective. Previous studies on emotion recognition from electroencephalography (EEG) mainly rely on single-channel-based feature extraction methods, which ignore the functional connectivity between brain regions. Hence, in this paper, we propose a novel emotion-relevant critical subnetwork selection algorithm and investigate three EEG functional connectivity network features: strength, clustering coefficient, and eigenvector centrality. Approach. After constructing the brain networks by the correlations between pairs of EEG signals, we calculated critical subnetworks through the average of brain network matrices with the same emotion label to eliminate the weak associations. Then, three network features were conveyed to a multimodal emotion recognition model using deep canonical correlation analysis along with eye movement features. The discrimination ability of the EEG connectivity features in emotion recognition is evaluated on three public datasets: SEED, SEED-V, and DEAP. Main results. The experimental results reveal that the strength feature outperforms the state-of-the-art features based on single-channel analysis. The classification accuracies of multimodal emotion recognition are
95.08
±
6.42
%
on the SEED dataset,
84.51
±
5.11
%
on the SEED-V dataset, and
85.34
±
2.90
%
and
86.61
±
3.76
%
for arousal and valence on the DEAP dataset, respectively, which all achieved the best performance. In addition, the brain networks constructed with 18 channels achieve comparable performance with that of the 62-channel network and enable easier setups in real scenarios. Significance. The EEG functional connectivity networks combined with emotion-relevant critical subnetworks selection algorithm we proposed is a successful exploration to excavate the information between channels.
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20
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Khare SK, Bajaj V. A self-learned decomposition and classification model for schizophrenia diagnosis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 211:106450. [PMID: 34619600 DOI: 10.1016/j.cmpb.2021.106450] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 09/28/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Schizophrenia (SZ) is a type of neurological disorder that is diagnosed by professional psychiatrists based on interviews and manual screening of patients. The procedures are time-consuming, burdensome, and prone to human error. This urgently necessitates the development of an effective and precise computer-aided design for the detection of SZ. One such efficient source for SZ detection is the electroencephalogram (EEG) signals. Because EEG signals are non-stationary, it is challenging to find representative information in its raw form. Decomposing the signals into multi-modes can provide detailed insight information from it. But the choice of uniform decomposition and hyper-parameters leads to information loss affecting system performance drastically. METHOD In this paper, automatic signal decomposition and classification methods are proposed for the detection of SZ and healthy control EEG signals. The Fisher score method is used for the selection of the most discriminant channel. Flexible tunable Q wavelet transform (F-TQWT) is developed for efficient decomposition of EEG signals by reducing root mean square error with grey wolf optimization (GWO) algorithm. Five features are extracted from the adaptively generated subbands and selected by the Kruskal Wallis test. The feature matrix is given as an input to the flexible least square support vector machine (F-LSSVM) classifier. The hyper-parameters and kernel of classifier are selected such that the accuracy of each subband is maximized using GWO algorithm. RESULTS The effectiveness and superiority of the proposed method is tested by evaluating seven performance parameters. An accuracy of 91.39%, sensitivity, specificity, precision, F-1 measure, false positive rate and error of 92.65%, 93.22%, 95.57%, 0.9306, 6.78% and 8.61% is achieved. The results prove superiority of the developed F-TQWT decomposition and F-LSSVM classifier over existing methodologies. CONCLUSION The EEG signals of healthy control and SZ subjects performing motor and auditory tasks simultaneously provide higher discrimination ability over the subjects performing auditory and motory tasks separately. The developed model is accurate, robust, and effective as it is developed on a relatively larger data-set, obtained maximum performance, and tested using ten-fold cross-validation technique. This proposed model is ready to be put to test for real-time SZ detection.
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Affiliation(s)
- Smith K Khare
- Electronics and Communication Discipline, Indian Institute of Information Technology Design and Manufacturing, Jabalpur, MP, 482005, India.
| | - Varun Bajaj
- Electronics and Communication Discipline, Indian Institute of Information Technology Design and Manufacturing, Jabalpur, MP, 482005, India
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21
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Subject-Specific Cognitive Workload Classification Using EEG-Based Functional Connectivity and Deep Learning. SENSORS 2021; 21:s21206710. [PMID: 34695921 PMCID: PMC8541420 DOI: 10.3390/s21206710] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 09/22/2021] [Accepted: 10/02/2021] [Indexed: 11/16/2022]
Abstract
Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other real-time and high-risk situations. Neuroimaging techniques have long been used for estimating cognitive workload. Given the portability, cost-effectiveness and high time-resolution of EEG as compared to fMRI and other neuroimaging modalities, an efficient method of estimating an individual’s workload using EEG is of paramount importance. Multiple cognitive, psychiatric and behavioral phenotypes have already been known to be linked with “functional connectivity”, i.e., correlations between different brain regions. In this work, we explored the possibility of using different model-free functional connectivity metrics along with deep learning in order to efficiently classify the cognitive workload of the participants. To this end, 64-channel EEG data of 19 participants were collected while they were doing the traditional n-back task. These data (after pre-processing) were used to extract the functional connectivity features, namely Phase Transfer Entropy (PTE), Mutual Information (MI) and Phase Locking Value (PLV). These three were chosen to do a comprehensive comparison of directed and non-directed model-free functional connectivity metrics (allows faster computations). Using these features, three deep learning classifiers, namely CNN, LSTM and Conv-LSTM were used for classifying the cognitive workload as low (1-back), medium (2-back) or high (3-back). With the high inter-subject variability in EEG and cognitive workload and recent research highlighting that EEG-based functional connectivity metrics are subject-specific, subject-specific classifiers were used. Results show the state-of-the-art multi-class classification accuracy with the combination of MI with CNN at 80.87%, followed by the combination of PLV with CNN (at 75.88%) and MI with LSTM (at 71.87%). The highest subject specific performance was achieved by the combinations of PLV with Conv-LSTM, and PLV with CNN with an accuracy of 97.92%, followed by the combination of MI with CNN (at 95.83%) and MI with Conv-LSTM (at 93.75%). The results highlight the efficacy of the combination of EEG-based model-free functional connectivity metrics and deep learning in order to classify cognitive workload. The work can further be extended to explore the possibility of classifying cognitive workload in real-time, dynamic and complex real-world scenarios.
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22
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Fan Y, Gan Q, Feng H, Yuan L. Comparison and Analysis of EEG with Negative and Positive Symptoms of Schizophrenia. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3339] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Schizophrenia is a type of major mental illness characterized by split personality in thinking and behaviour. Schizophrenia has become the fourth leading cause of disability among adolescents in developing countries. This article is based on the EEG signals of normal people, schizophrenia-positive
patients, and schizophrenianegative patients. From the neuropsychological background and cognitive ability test results, the level of EEG amplitude, the frequency distribution of EEG in performing functional tasks, and the analysis of a comparative analysis of the degree of brain injury can
help medical staff to accurately and effectively classify and diagnose the different symptoms of schizophrenia.
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Affiliation(s)
- Yinyan Fan
- Department of Psychiatry, Huangshi Second Hospital, Huangshi, Hubei, 435000, People’s Republic of China
| | - Quanxi Gan
- Department of Psychiatry, Huangshi Second Hospital, Huangshi, Hubei, 435000, People’s Republic of China
| | - Huibin Feng
- Department of Intensive Care Unit, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Edong Healthcare Group, Hubei, 435000, People’s Republic of China
| | - Lixue Yuan
- Department of Intensive Care Unit, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Edong Healthcare Group, Hubei, 435000, People’s Republic of China
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23
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Chen L, Liu X, Xuan B, Zhang J, Liu Z, Zhang Y. Selection of EMG Sensors Based on Motion Coordinated Analysis. SENSORS 2021; 21:s21041147. [PMID: 33562131 PMCID: PMC7915866 DOI: 10.3390/s21041147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 02/01/2021] [Accepted: 02/03/2021] [Indexed: 11/16/2022]
Abstract
The intelligent prosthesis driven by electromyography (EMG) signal provides a solution for the movement of the disabled. The proper position of EMG sensors can improve the prosthesis's motion recognition ability. To exert the amputee's action-oriented ability and the prosthesis' control ability, the EMG spatial distribution and internal connection of the prosthetic wearer is analyzed in three kinds of movement conditions: appropriate angle, excessive angle, and angle too small. Firstly, the correlation characteristics between the EMG channels are analyzed by mutual information to construct a muscle functional network. Secondly, the network's features of different movement conditions are analyzed by calculating the characteristic of nodes and evaluating the importance of nodes. Finally, the convergent cross-mapping method is applied to construct a directed network, and the critical muscle groups which can reflect the user's movement intention are determined. Experiment shows that this method can accurately determine the EMG location and simplify the distribution of EMG sensors inside the prosthetic socket. The network characteristics of key muscle groups can distinguish different movements effectively and provide a new strategy for decoding the relationship between limb nerve control and body movement.
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Affiliation(s)
- Lingling Chen
- School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300131, China; (L.C.); (X.L.); (Z.L.); (Y.Z.)
- Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology, Ministry of Education, Tianjin 300131, China
| | - Xiaotian Liu
- School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300131, China; (L.C.); (X.L.); (Z.L.); (Y.Z.)
| | - Bokai Xuan
- School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300131, China; (L.C.); (X.L.); (Z.L.); (Y.Z.)
- Correspondence: ; Tel.: +86-1382-155-7991
| | - Jie Zhang
- School of Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne NE1 7RU, UK;
| | - Zuojun Liu
- School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300131, China; (L.C.); (X.L.); (Z.L.); (Y.Z.)
- Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology, Ministry of Education, Tianjin 300131, China
| | - Yan Zhang
- School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300131, China; (L.C.); (X.L.); (Z.L.); (Y.Z.)
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24
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Arithmetic success and gender-based characterization of brain connectivity across EEG bands. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102222] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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25
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An EEG-based methodology for the estimation of functional brain connectivity networks: Application to the analysis of newborn EEG seizure. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102229] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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26
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Chang W, Wang H, Yan G, Lu Z, Liu C, Hua C. EEG based functional connectivity analysis of human pain empathy towards humans and robots. Neuropsychologia 2020; 151:107695. [PMID: 33245968 DOI: 10.1016/j.neuropsychologia.2020.107695] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 11/12/2020] [Accepted: 11/21/2020] [Indexed: 11/30/2022]
Abstract
Humans can show emotional reactions toward humanoid robots, such as empathy. Previous neuroimaging studies have indicated that neural responses of empathy for others' pain are modulated by an early automatic emotional sharing and a late controlled cognitive evaluation process. Recent studies about pain empathy for robots found humans present similar empathy process towards humanoid robots under painful stimuli as well as to humans. However, the whole-brain functional connectivity and the spatial dynamics of neural activities underlying empathic processes are still unknown. In the present study, the functional connectivity was investigated for ERPs recorded from 18 healthy adults who were presented with pictures of human hand and robot hand under painful and non-painful situations. Functional brain networks for both early and late empathy responses were constructed and a new parameter, empathy index (EI), was proposed to represent the empathy ability of humans quantitatively. We found that the mutual dependences between early ERP components was significantly decreased, but for the late components, there were no significant changes. The mutual dependences for human hand stimuli were larger than to robot hand stimuli for early components, but not for late components. The connectivity weights for early components were larger than late components. EI value shows significant difference between painful and non-painful stimuli, indicating it is a good indicator to represent the empathy of humans. This study enriches our understanding of the neurological mechanisms implicated in human empathy, and provides evidence of functional connectivity for both early and late responses of pain empathy towards humans and robots.
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Affiliation(s)
- Wenwen Chang
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, 730070, Lanzhou, China.
| | - Hong Wang
- School of Mechanical Engineering and Automation, Northeastern University, 110819, Shenyang, China.
| | - Guanghui Yan
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, 730070, Lanzhou, China.
| | - Zhiguo Lu
- School of Mechanical Engineering and Automation, Northeastern University, 110819, Shenyang, China.
| | - Chong Liu
- School of Mechanical Engineering and Automation, Northeastern University, 110819, Shenyang, China.
| | - Chengcheng Hua
- School of Automation, Nanjing University of Information Science & Technology, Nanjing, 210044, China.
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27
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Chang Q, Liu M, Tian Q, Wang H, Luo Y, Zhang J, Wang C. EEG-Based Brain Functional Connectivity in First-Episode Schizophrenia Patients, Ultra-High-Risk Individuals, and Healthy Controls During P50 Suppression. Front Hum Neurosci 2019; 13:379. [PMID: 31803031 PMCID: PMC6870009 DOI: 10.3389/fnhum.2019.00379] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 10/10/2019] [Indexed: 01/29/2023] Open
Abstract
Dysfunctional processing of auditory sensory gating has generally been found in schizophrenic patients and ultra-high-risk (UHR) individuals. The aim of the study was to investigate the differences of functional interaction between brain regions and performance during the P50 sensory gating in UHR group compared with those in first-episode schizophrenia patients (FESZ) and healthy controls (HC) groups. The study included 128-channel scalp Electroencephalogram (EEG) recordings during the P50 auditory paradigm for 35 unmedicated FESZ, 30 drug-free UHR, and 40 HC. Cortical sources of scalp electrical activity were recomputed using exact low-resolution electromagnetic tomography (eLORETA), and functional brain networks were built at the source level and compared between the groups (FESZ, UHR, HC). A classifier using decision tree was designed for differentiating the three groups, which uses demographic characteristics, MATRICS Consensus Cognitive Battery parameters, behavioral features in P50 paradigm, and the measures of functional brain networks based on graph theory during P50 sensory gating. The results showed that very few brain connectivities were significantly different between FESZ and UHR groups during P50 sensory gating, and that a large number of brain connectivities were significantly different between FESZ and HC groups and between UHR and HC groups. Furthermore, the FESZ group had a stronger connection in the right superior frontal gyrus and right insula than the HC group. And the UHR group had an enhanced connection in the paracentral lobule and the middle temporal gyrus compared with the HC group. Moreover, comparison of classification analysis results showed that brain network metrics during P50 sensory gating can improve the accuracy of the classification for FESZ, UHR and HC groups. Our findings provide insight into the mechanisms of P50 suppression in schizophrenia and could potentially improve the performance of early identification and diagnosis of schizophrenia for the earliest intervention.
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Affiliation(s)
- Qi Chang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China
| | - Meijun Liu
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China
| | - Qing Tian
- The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Hua Wang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.,Hefei Innovation Research Institute, Beihang University, Hefei, China.,School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Yu Luo
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China
| | - Jicong Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, China.,Hefei Innovation Research Institute, Beihang University, Hefei, China.,School of Biomedical Engineering, Anhui Medical University, Hefei, China
| | - Chuanyue Wang
- The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
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28
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Performance of machine learning methods applied to structural MRI and ADAS cognitive scores in diagnosing Alzheimer’s disease. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.08.009] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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29
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Chang W, Wang H, Hua C, Wang Q, Yuan Y. Comparison of different functional connectives based on EEG during concealed information test. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.12.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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30
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Cai L, Wei X, Wang J, Yu H, Deng B, Wang R. Reconstruction of functional brain network in Alzheimer's disease via cross-frequency phase synchronization. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.07.019] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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31
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32
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Sun C, Yang F, Wang C, Wang Z, Zhang Y, Ming D, Du J. Mutual Information-Based Brain Network Analysis in Post-stroke Patients With Different Levels of Depression. Front Hum Neurosci 2018; 12:285. [PMID: 30065639 PMCID: PMC6056615 DOI: 10.3389/fnhum.2018.00285] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 06/25/2018] [Indexed: 11/15/2022] Open
Abstract
Post-stroke depression (PSD) is the most common stroke-related emotional disorder, and it severely affects the recovery process. However, more than half cases are not correctly diagnosed. This study was designed to develop a new method to assess PSD using EEG signal to analyze the specificity of PSD patients' brain network. We have 107 subjects attended in this study (72 stabilized stroke survivors and 35 non-depressed healthy subjects). A Hamilton Depression Rating Scale (HDRS) score was determined for all subjects before EEG data collection. According to HDRS score, the 72 patients were divided into 3 groups: post-stroke non-depression (PSND), post-stroke mild depression (PSMD) and post-stroke depression (PSD). Mutual information (MI)-based graph theory was used to analyze brain network connectivity. Statistical analysis of brain network characteristics was made with a threshold of 10-30% of the strongest MIs. The results showed significant weakened interhemispheric connections and lower clustering coefficient in post-stroke depressed patients compared to those in healthy controls. Stroke patients showed a decreasing trend in the connection between the parietal-occipital and the frontal area as the severity of the depression increased. PSD subjects showed abnormal brain network connectivity and network features based on EEG, suggesting that MI-based brain network may have the potential to assess the severity of depression post stroke.
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Affiliation(s)
- Changcheng Sun
- Rehabilitation Medical Department, Tianjin Union Medical Centre, Tianjin, China
| | - Fei Yang
- Department of Health and Exercise Science, Tianjin University of Sport, Tianjin, China
| | - Chunfang Wang
- Rehabilitation Medical Department, Tianjin Union Medical Centre, Tianjin, China
| | - Zhonghan Wang
- Rehabilitation Medical Department, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Ying Zhang
- Rehabilitation Medical Department, Tianjin Union Medical Centre, Tianjin, China
| | - Dong Ming
- Department of Biomedical Engineering, College of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, China
| | - Jingang Du
- Rehabilitation Medical Department, Tianjin Union Medical Centre, Tianjin, China
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